This invention relates generally to social networking, and in particular to providing user metrics for an unknown dimension to an external system.
Traditional advertisers have used focus groups and demographic data to gain an understanding of how to design and implement effective ad campaigns. For example, a sports drink advertiser may use a random sampling of a target population, such as 18-35 year-old men, to determine whether an ad would be effective. An advertisement is then presented to thousands, or even millions of people, but advertisers may be left wondering how different sets of consumers are responding to the advertisement. As a result, information about the population of users that responded to the advertisement cannot be obtained through traditional methods.
In recent years, social networking systems have made it easier for users to share their interests and preferences in real-world concepts, such as their favorite movies, musicians, celebrities, brands, hobbies, sports teams, and activities. These interests may be declared by users in user profiles and may also be inferred by social networking systems. Users can also interact with these real-world concepts through multiple communication channels on social networking systems, including interacting with pages on the social networking system, sharing interesting articles about causes and issues with other users on the social networking system, and commenting on actions generated by other users on objects external to the social networking system. Although advertisers may have some demographics information about users who are interested in their brands, this information is often incomplete or based on samples of aggregated data.
Specifically, users that perform actions on external systems, such as expressing an interest in a specific type of new car, or clicking through an application to buy shoes, are only identifiable by external systems through non-person level mechanisms such as cookies or browsers that are not able to be tied to the actual people performing the actions. A social networking system, on the other hand, is able to identify people and may record information about users that is valuable to administrators of external systems, such as prior ad exposure, other interests, and connectedness among other users of the social networking system. The social networking system may also have the ability to track user actions that are performed on external systems. However, existing systems have not provided efficient mechanisms of providing user metrics on these user actions without compromising the privacy and anonymity of its users. Tools are needed to provide advertisers with useful information about users without sacrificing the privacy and anonymity of users the social networking system.